The effect of peripheral high-frequency electrical stimulation on the primary somatosensory cortex in pigs

This study implements the use of Danish Landrace pigs as subjects for the long-term potentiation (LTP)-like pain model. This is accomplished by analyzing changes in the primary somatosensory cortex (S1) in response to electrical stimulation on the ulnar nerve after applying high-frequency electrical stimulation (HFS) on the ulnar nerve. In this study, eight Danish Landrace pigs were electrically stimulated, through the ulnar nerve, to record the cortically evoked response in S1 by a 16-channel microelectrode array (MEA). Six of these pigs were subjected to HFS (four consecutive, 15 mA, 100 Hz, 1000 µs pulse duration) 45 min after the start of the experiment. Two pigs were used as control subjects to compare the cortical response to peripheral electrical stimulation without applying HFS. Low-frequency components of the intracortical signals (0.3–300 Hz) were analyzed using event-related potential (ERP) analysis, where the minimum peak during the first 30–50 ms (N1 component) in each channel was detected. The change in N1 was compared over time across the intervention and control groups. Spectral analysis was used to demonstrate the effect of the intervention on the evoked cortical oscillations computed between 75 ms and 200 ms after stimulus. ERP analysis showed an immediate increase in N1 amplitude that became statistically significant 45 mins after HFS (p < 0.01) for the intervention group. The normalized change in power in frequency oscillations showed a similar trend. The results show that the LTP-like pain model can be effectively implemented in pigs using HFS since the cortical responses are comparable to those described in humans

The Use of the Velocity Selective Recording Technique to Reveal the Excitation Properties of the Ulnar Nerve in Pigs

Decoding information from the peripheral nervous system via implantable neural interfaces remains a significant challenge, considerably limiting the advancement of neuromodulation and neuroprosthetic devices. The velocity selective recording (VSR) technique has been proposed to improve the classification of neural traffic by combining temporal and spatial information through a multi-electrode cuff (MEC). Therefore, this study investigates the feasibility of using the VSR technique to characterise fibre type based on the electrically evoked compound action potentials (eCAP) propagating along the ulnar nerve of pigs in vivo. A range of electrical stimulation parameters (amplitudes of 50 μA–10 mA and pulse durations of 100 μs, 500 μs, 1000 μs, and 5000 μs) was applied on a cutaneous and a motor branch of the ulnar nerve in nine Danish landrace pigs. Recordings were made with a 14 ring MEC and a delay-and-add algorithm was used to convert the eCAPs into the velocity domain. The results revealed two fibre populations propagating along the cutaneous branch of the ulnar nerve, with mean velocities of 55 m/s and 21 m/s, while only one dominant fibre population was found for the motor branch, with a mean velocity of 63 m/s. Because of its simplicity to provide information on the fibre selectivity and direction of propagation of nerve fibres, VSR can be implemented to advance the performance of the bidirectional control of neural prostheses and bioelectronic medicine applications

High-density electromyography investigated by linear mixed-effects models

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2018.O sinal eletromiográfico é o registro da atividade elétrica nas fibras musculares ativas durante o processo de contração muscular. A Eletromiografia de Alta Densidade (HD-sEMG) utiliza uma matriz (e.g., 8x4) de eletrodos com distância inter-eletrodos que varia de 4 a 10 mm. Dentre as inúmeras aplicações do HD-sEMG, está a utilização na investigação das alterações neuromusculares ocasionadas pelo Diabetes Mellitus (DM) e Neuropatia Diabética Periférica (NDP). Neste contexto, o sinal de HD-sEMG é registrado durante a Contração Isométrica Voluntária Máxima (CIVM) de dorsiflexão do tornozelo. A característica dos sinais de HD-sEMG implica em medidas repetidas, ocasionando em uma análise de dados que não permite assumir independência entre observações, que é uma suposição necessária para a utilização de métodos estatísticos convencionais. Assim, os Modelos Lineares Mistos (MLM) surgem como uma ferramenta apropriada para tratar adequadamente a dependência entre as observações. O objetivo deste trabalho é utilizar os MLM para avaliar as principais variáveis de HD-sEMG e também avaliar a repetibilidade das medições de CIVM em um estudo de NDP. O sinal de HD-sEMG foi coletado em 50% da CIVM por 25s. Os indivíduos foram divididos em três grupos: Controle (n=6), DM (n=6) e DM com NDP (n=5). Foram analisadas as seguintes variáveis, em épocas de 0,5s: Valor Médio Quadrático (RMS), Valor Retificado Médio (ARV), Frequência Média (MNF), Frequência Mediana (MDF), Entropia Modificada (ME) e Coeficiente de Variação (COV). Para a repetibilidade da CIVM, dez indivíduos saudáveis realizaram a CIVM duas vezes ao dia por três dias consecutivos. A construção dos modelos foi feita pela metodologia top-down e as comparações dos modelos por testes da razão de verossimilhança. Para cada uma das variáveis de HD-sEMG, o modelo incluiu estruturas de dependência com termos aleatórios resultando em um melhor ajuste dos modelos quando comparado a um modelo tradicional com apenas efeitos fixos (p<0.05). Quanto a repetibilidade da CIVM, o coeficiente de correlação intraclasse resultou em um valor de 86,6% o que é considerado um excelente nível de repetibilidade. Os resultados obtidos neste trabalho demonstram que as variáveis de HD-sEMG avaliadas com MLM são melhores representadas quando as estruturas de dependência são levadas em consideração, em comparação com métodos tradicionais utilizando apenas efeitos fixos. Além disso, os resultados também demonstram que a CIVM da dorsiflexão do tornozelo é uma medida altamente repetível.Abstract : The Electromyographic (EMG) signal is the recording of electrical activity in active muscle fibres during a contraction process. The High-Density Electromyography (HD-sEMG) uses a matrix (e.g., 8x4) of electrodes with inter-electrode distances ranging from 4 up to 10 mm. Among the large number of applications using HD-sEMG, is the investigation of neuromuscular alterations caused by Diabetes Mellitus (DM) and Diabetic Peripheral Neuropathy (DPN). In this scenario, the HD-sEMG signal is usually obtained during Maximum Voluntary Isometric Contraction (MVIC) of ankle dorsiflexion. The characteristic of the HD-sEMG signal implies in repeated measures, resulting in an analysis that cannot assume independence between observations, which is a basic assumption required by traditional statistical methods. In this way, Linear Mixed Effects (LME) models provide an adequate tool to handle the dependency between observations. The objective of this work is to apply LME models to assess the main variables used in HD-sEMG and also to evaluate the reliability of the MVIC in an application regarding DPN. The HD-sEMG signal was obtained from 50% of the MVIC for 25s. The individuals were divided into three groups: Control (n=6), DM (n=6) and DM with DPN (n=5). The following variables were analysed with epochs of 0.5s: The following variables were analysed with epochs of 0.5s: Root Mean Square (RMS), Average Rectified Value (ARV), Mean Frequency (MNF), Median Frequency (MDF), Modified Entropy (ME) and Coefficient of Variation (COV). For the reliability of the MVIC, ten healthy subjects performed the MVIC twice a day for three consecutive days. The top-down strategy for model selection process was implemented and the model comparison was performed through a likelihood ratio test. For each variable, the model included structures of dependency with random effects resulting in a better model adequacy when compared to a model with only fixed effects (p<0.05). For the reliability of the MVIC, the intraclass correlation coefficient resulted in a value of 86.6%, which is considered an excellent level of reliability. The results obtained in this work show that the HD-sEMG variables evaluated with LME models are better represented when dependency structures are taken into account, in comparison to traditional models using only fixed effects. Moreover, the results also demonstrate that MVIC of the ankle dorsiflexion is a highly repeatable measure